UnslothBLOCK

Intelligent Skill routing and workflow orchestration for AI agents — +21.12 pp reward, −29.6% tokens on SkillsBench with DeepSeekV4Flash-VE.

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: unsloth
description: Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Fine-Tuning, Unsloth, Fast Training, LoRA, QLoRA, Memory-Efficient, Optimization, Llama, Mistral, Gemma, Qwen]
dependencies: [unsloth, torch, transformers, trl, datasets, peft]
---

# Unsloth Skill

Comprehensive assistance with unsloth development, generated from official documentation.

## When to Use This Skill

This skill should be triggered when:
- Working with unsloth
- Asking about unsloth features or APIs
- Implementing unsloth solutions
- Debugging unsloth code
- Learning unsloth best practices

## Quick Reference

### Common Patterns

*Quick reference patterns will be added as you use the skill.*

## Reference Files

This skill includes comprehensive documentation in `references/`:

- **llms-txt.md** - Llms-Txt documentation

Use `view` to read specific reference files when detailed information is needed.

## Working with This Skill

### For Beginners
Start with the getting_started or tutorials reference files for foundational concepts.

### For Specific Features
Use the appropriate category reference file (api, guides, etc.) for detailed information.

### For Code Examples
The quick reference section above contains common patterns extracted from the official docs.

## Resources

### references/
Organized documentation extracted from official sources. These files contain:
- Detailed explanations
- Code examples with language annotations
- Links to original documentation
- Table of contents for quick navigation

### scripts/
Add helper scripts here for common automation tasks.

### assets/
Add templates, boilerplate, or example projects here.

## Notes

- This skill was automatically generated from official documentation
- Reference files preserve the structure and examples from source docs
- Code examples include language detection for better syntax highlighting
- Quick reference patterns are extracted from common usage examples in the docs

## Updating

To refresh this skill with updated documentation:
1. Re-run the scraper with the same configuration
2. The skill will be rebuilt with the latest information

<!-- Trigger re-upload 1763621536 -->

BLOCKgrade D · trust 69/100 Do not install this without reading the findings. The audit found something that could harm you or your machine.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (18)

| **Gemma 3n** | E2B           | [link](https://huggingface.co/unsloth/gemma-3n-E2B-it-GGUF) | [link](https://huggingface.co/unsloth/gemma-3n-E2B-it-unsloth-bnb-4bit)      |

* **DeepSeek-R1-Distill-Llama-8B** is a fine-tuned version of Llama-3.1-8B. DeepSeek utilized data generated by DeepSeek-R1, to fine-tune Llama-3.1-8B. This process, known as distillation (a subcatego

--net=host \

We can now save the finetuned model as a small 100MB file called a LoRA adapter like below. You can instead push to the Hugging Face hub as well if you want to upload your model! Remember to get a Hug

You can save the finetuned model as a small 100MB file called a LoRA adapter like below. You can instead push to the Hugging Face hub as well if you want to upload your model! Remember to get a Huggin

We can now save the finetuned model as a small 100MB file called a LoRA adapter like below. You can instead push to the Hugging Face hub as well if you want to upload your model! Remember to get a Hug

-e "SSH_KEY=$(cat ~/.ssh/container_key.pub)"

-e "SSH_KEY=$(cat ~/.ssh/container_key.pub)" \

-e "SSH_KEY=$(cat ~/.ssh/container_key.pub)" \

-e "SSH_KEY=$(cat ~/.ssh/container_key.pub)" \

-e "SSH_KEY=$(cat ~/.ssh/container_key.pub)" \

Read more on running Llama 4 here: <https://docs.unsloth.ai/basics/tutorial-how-to-run-and-fine-tune-llama-4>

If you're looking to install Conda in a Linux environment, [read here](https://docs.anaconda.com/miniconda/), or run the below:

curl -fsSL https://ollama.com/install.sh | sh

curl -fsSL https://ollama.com/install.sh | sh

curl -LsSf https://astral.sh/uv/install.sh | sh && source $HOME/.local/bin/env

curl -fsSL https://ollama.com/install.sh | sh

curl -fsSL https://ollama.com/install.sh | sh

Gates applied: no_behavioural_pass.